Automated Identification of Competing Narratives in Political Discourse on Social Media
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Computer Science > Computation and Language
Title:Automated Identification of Competing Narratives in Political Discourse on Social Media
Abstract:Social media platforms have become central to shaping political discourse, serving as arenas where narratives form and evolve, influencing public opinion. Identifying and analyzing these narratives, particularly when they compete across different political ideologies, is crucial for understanding the dynamics of modern political communication. This paper presents an unsupervised framework for identifying and characterizing competing narratives in political discourse on social media, focusing on German politicians' tweets. The framework employs a multi-stage pipeline that integrates natural language processing techniques such as topic modeling, event detection, and event linking. By forming data into coherent stories and uncovering the distinct perspectives of user communities, the system is able to detect the key competing narratives, highlighting the divergent framings and conflicts surrounding trending political topics. Two case studies on polarizing political issues demonstrate the efficacy of the methodology, showcasing its ability to uncover and analyze divergent viewpoints. The findings contribute to the broader understanding of how narratives propagate within the digital public sphere and offer insights for policymakers, social media platforms, and researchers interested in monitoring political discourse.
| Comments: | 11 pages, 5 figures. Published in the proceedings of Text2Story 2025, held with ECIR 2025 |
| Subjects: | Computation and Language (cs.CL); Social and Information Networks (cs.SI) |
| Cite as: | arXiv:2609.11202 [cs.CL] |
| (or arXiv:2609.11202v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2609.11202
arXiv-issued DOI via DataCite (pending registration)
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| Journal reference: | In: Proceedings of Text2Story - Eighth Workshop on Narrative Extraction From Texts (Text2Story 2025), CEUR Workshop Proceedings, Vol. 3964, 2025, pp. 137-147 |
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